Data Journalism Lecture Notes

These flashcards cover the definition, history, forms, and distinctions of data journalism as presented in the lecture by Dr. Fatima I. Abubakre.

W. Edwards Deming

The individual who famously stated, 'In God we trust. All others must bring data,' emphasizing the importance of data measurement and analysis.

Data Journalism

A field also known as data-driven journalism that uses data from multiple sources, combining traditional investigative skills with data analysis to uncover hidden information and tell compelling stories.

Ida B. Wells

An American investigative journalist (July 16, 1862- March 25, 1931) who used data journalism techniques in the 1890s to compile and analyze data regarding lynchings of Black men.

Memphis Free Speech

The newspaper where Ida B. Wells published her data-driven investigative articles exposing the lies behind lynchings.

Quantitative Journalism

The use of numerical data and statistical analysis in journalistic reporting to uncover patterns, trends, and insights for evidence-based storytelling.

Computer-Assisted Reporting (CAR)

A form of journalism that emerged in the 1970s and gained prominence in the late 1980s and early 1990s, involving the use of computer tools, empirical methods like surveys, and statistical analysis.

Data Journalism (Specific Form)

A form of journalism that emerged in the late 2000s characterized by the use of open data, open-source tools, and the mass democratization of resources for storytelling with numbers.

Computational Journalism

The application of computing and computational thinking to information gathering and presentation, utilizing algorithms, data, and tools like $R$ and Python.

Open-source principles

A point of overlap between data journalism and computational journalism that emphasizes the use of open data and tools to allow for collaboration, transparency, and innovation.

Cultural Background Divergence

The distinction where CAR emerged from social science and investigative journalism, whereas data and computational journalism arose from the intersection of professional journalism and open-source culture.

Epistemological Approach Divergence

The difference between CAR’s traditional, hypothesis-driven approach and the emphasis of data/computational journalism on open data, programming skills, and computational thinking.

Participation and Control Divergence

The contrast between CAR’s orientation toward professional expertise and institutional production versus the networking and collaboration between professionals and non-professionals in data/computational journalism.

Data Gathering and Analysis Divergence

The shift from CAR’s targeted sampling and inference-based analysis to data/computational journalism’s focus on large datasets, exploratory analysis, and prioritizing correlation over causation.

Role of the Public in CAR

A passive role where the public responds with moral outrage to investigative stories to uphold community values, rather than active participation in data analysis.

Role of the Public in Data Journalism

A direct and active role where the audience is provided access to data and tools to perform their own searches, analysis, and interpretation to understand public issues.